SDCI Data: Improvement: Java Graphical Authorship Attribution Program (JGAAP)

SDCI 数据：改进：Java 图形作者归属计划 (JGAAP)

• 批准号: 1032683
• 负责人: Patrick Juola
• 金额: $ 162.2万
• 依托单位: Duquesne University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-09-01 至 2014-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1032683&HistoricalAwards=false
• 关键词: SDCI; Data; Improvement; Java; Graphical

Recent developments in machine learning and corpus linguistics have shown it to be possible to make automatic determinations about authorship using statistics; the NSF- funded JGAAP (Java Graphical Authorship Attribution Program) system has been part of these developments. JGAAP has helped support the emerging authorship attribution community and create a useful tool for a wide variety of scholastic specialties. Although JGAAP incorporates thousands of possible methods, there are many more in the literature that have been proposed but not rigorously tested. Comparative testing on a large scale will require the development of new methods and test corpora. In addition, there are many key problems to address to meet the needs of the community, such as the open class problem, the adversarial problem, and the coauthorship problem. Finally, we will examine applications of JGAAP and similar systems to key areas in linguistic profiling, such as determining gender, education, native language, psychological profile, medical condition, age (of document or writer), or even attempted deceptiveness. Again, by applying a rigorous testing method to these new problems and corpora, the project can establish accuracy benchmarks for various techniques (under the various testing conditions), find new combinations resulting in improved techniques, and establish a recommendation for 'best practices.' Improved authorship attribution will be immediately useful both to scholars and in broader social contexts, such as law enforcement and forensics where there are direct demands for this kind of security technology. The historical/social analysis will also provide better access between the related disciplines of digital humanities, sociology, history, and computer science, providing the basis for a better understanding of traditional humanities issues. Profiling work can help medical and psychological practitioners by providing a non-invasive method to detect certain aspects of a person's mind. The software developed (and the planned development/distribution process) will help improve the effectiveness of both digital humanities scholarship and computer science, especially through the establishment of software review standards and processes. In particular, by providing direct evidence of the conditions and expected error rates involved in various techniques, the information gained will help authorship attribution meet the Daubert criteria for expert evidence, allowing authorship attribution to be used in a formal legal setting. Finally, the funding of this research will help support the unique interdisciplinary Duquesne University Computational Mathematics program, providing a broader access to an unusual and atypical audience for technological education.

机器学习和语料库语言学的最新发展表明，使用统计数据自动确定作者身份是可能的; NSF资助的JGAAP（Java图形作者归属程序）系统是这些发展的一部分。 JGAAP帮助支持新兴的作者归属社区，并为各种学术专业创造了有用的工具。虽然JGAAP包含了数千种可能的方法，但文献中还有更多的方法已经提出，但没有经过严格的测试。大规模的比较测试需要开发新的方法和测试语料库。此外，还有许多关键问题需要解决，以满足社区的需求，如公开课问题，对抗性问题和合作问题。最后，我们将研究JGAAP和类似系统在语言特征分析关键领域的应用，例如确定性别，教育，母语，心理特征，医疗条件，年龄（文件或作者），甚至是企图欺骗。 同样，通过对这些新问题和语料库应用严格的测试方法，该项目可以为各种技术（在各种测试条件下）建立准确性基准，找到导致改进技术的新组合，并建立“最佳实践”的建议。“改进的作者归属将立即对学者和更广泛的社会背景有用，例如对这种安全技术有直接需求的执法和法医。历史/社会分析还将提供数字人文，社会学，历史和计算机科学的相关学科之间的更好的访问，为更好地理解传统人文问题提供基础。侧写工作可以通过提供一种非侵入性的方法来检测一个人思想的某些方面，从而帮助医学和心理学从业者。开发的软件（以及计划的开发/分发过程）将有助于提高数字人文奖学金和计算机科学的有效性，特别是通过建立软件审查标准和流程。特别是，通过提供各种技术所涉及的条件和预期错误率的直接证据，所获得的信息将有助于作者归属满足Daubert专家证据标准，允许在正式的法律的环境中使用作者归属。最后，这项研究的资金将有助于支持独特的跨学科迪克讷大学计算数学计划，为技术教育提供更广泛的不寻常和非典型的受众。